Artificial Intelligence · Cross-listed from cs.CV
MatReplace: A Reference-Free, Conditioning-Aligned Benchmark for Material Replacement in Interior Scenes
Mingzhe Du, Thong Thanh Nguyen, Nguyen Tran Cong Duy et al. (+2 more)
Material replacement is a common interior-design operation: changing the material of a selected surface while preserving its geometry, surroundings, and illumination. Despite its commercial relevance, no public
benchmark isolates this task, and evaluating it is challenging. Reference-based metrics penalize valid outputs in this inherently one-to-many setting, favor the style of the reference generator, and cannot fairly compare editors that receive different forms of guidance. We introduce MatReplace, a reference-free
benchmark that evaluates edits along four verifiable dimensions: local material correctness, global lighting harmony, outside preservation, and inside structure. It defines three tracks that vary one conditioning signal at a time: (A) instruction only, (B) instruction plus region mask, and (C) material reference image instead of instruction. Our results reveal a clear divide between naming and visually grounding materials. In Track A, leading closed-source editors achieve exemplar-level material rendering and surpass the exemplar anchor under our primary aggregate. In Track B, masks help only mask-compatible models with weak scene preservation, with task-paired, single-seed effects ranging from +0.137 to -0.090 across aligned model families. In Track C, reference-image conditioning degrades every family under both aggregates, by -0.031 to -0.508; in the worst cases, models repaint the reference image itself and perform worse than returning the input unchanged. Thus, named-material rendering is largely solved by the strongest closed editors on this distribution, but grounding materials from pixels remains an open challenge. Expert ratings validate our ranking (Kendall's tau = 0.68) and align with our aggregates more closely than GT-referenced or CLIP-based baselines.
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All authors Mingzhe Du, Thong Thanh Nguyen, Nguyen Tran Cong Duy, See-Kiong Ng, Luu Anh Tuan
Artificial Intelligence
ACE: A Self-Correcting Agentic Canvas Editor for Multi-Slide Presentation Automation
JooYoung Jang, Taegyeong Lee, Jihyeon Park et al. (+1 more)
Commercial design platforms increasingly edit documents through large
language model (LLM)
agents, but two practical problems block reliable deployment: legacy document formats expose only flat, absolutely positioned elements, so
agents must recompute coordinates and routinely break layouts; and design has no unique ground truth, so diff-against-reference metrics penalize valid-but-different outputs. We present ACE, an agentic canvas editor over a hierarchical scene-graph with a presentation-specialized action space (98 tools), paired with CARE, a content-aware router that feeds the
agent only the relevant slice of each deck (avg.\ $\sim$89% input-
token reduction), and a self-correction loop driven by a ground-truth-free instruction-following (IF) judge whose natural-
language critique is fed back as the next-turn instruction. With a fixed backbone, a scene-graph editor in a single turn already matches a same-backbone agentic HTML pipeline that iterates internally; adding self-correction lifts ACE significantly above it on instruction following (IF 4.23 vs.\ 3.81 on the full 94-task
benchmark, paired $p{=}.010$, replicated by an out-of-loop judge) at 1.75$\times$ the speed and $\sim$44% lower cost. VQ means are statistically indistinguishable, but 26 blind raters prefer ACE overall (58.7% decisive win-rate) and prefer the self-corrected output 81% of the time; the ranking is invariant across three judge families, and out-of-loop judges retain two-thirds of the self-correction gain, bounding circularity. 66% of cases halt after one pass, and a strict-peak rollback removes every observed regression.
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All authors JooYoung Jang, Taegyeong Lee, Jihyeon Park, Nojun Kwak
Artificial Intelligence
Are Android GUI Agents Robust Against Runtime Anomalies? AnTrap: Evaluating Agents in Dynamic Adversarial Environments
Guo Gan, Yilun Zhao, Cong Chen et al. (+5 more)
GUI
agents often encounter dynamic anomalies when deployed on Android devices, from unexpected pop-ups to action misuse, yet existing
benchmarks lack systematic evaluation of
agent robustness against runtime anomalies. We introduce AnTrap, a comprehensive
benchmark that injects dynamic perturbations into
agent execution trajectories. We propose a taxonomy organizing real-world anomalies into four layers (State, Thinking, Action and Round) with ten fine-grained subcategories, and develop a construction pipeline that preserves task solvability while introducing realistic
adversarial conditions. Evaluating 16 leading GUI models, we reveal universal vulnerability to dynamic anomalies, with even the strongest models suffering significant performance degradation. Furthermore, we conduct GRPO training in both original and
adversarial environments to validate our
benchmark, separating environment-learnable anomalies from
reasoning-bottlenecked ones. Our findings show that while single-step traps at state and action layers are largely addressable through
adversarial reinforcement learning, deep contextual traps, like state deadlock, expose intrinsic limitations that cannot be resolved by training in environments with traps alone.
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All authors Guo Gan, Yilun Zhao, Cong Chen, Jinbiao Wei, Tingyu Song, Zheyuan Yang, Lin Fu, Hong Zhou
Artificial Intelligence · Cross-listed from cs.LG
Knowing When to Ask for Help: Bayesian Self-Escalation in Hierarchical LLM Agents
Nadeem Shaikh
Current LLM
agent systems decide delegation before
reasoning begins (a router picks a model) or after a response is complete (a verifier scores it and may retry). We study a third regime: an
agent that recognises, during its own
reasoning, that it is unlikely to succeed and transfers control to a stronger model. We formulate intra-generation delegation as a
Bayesian optimal-stopping problem over a learned competence posterior – an online estimate of the
agent's eventual task success whose sufficient statistics are learned from labelled trajectories, not read off raw
entropy. We derive the myopic escalation threshold in closed form, characterise the optimal policy via dynamic programming, and prove that the optimal policy is a time-varying threshold with no shape assumption on the raw signal. We further prove exponential separation of the oracle belief at the Chernoff-information rate of the signal, a regret bound governed by the calibration of the posterior, and a finite-sample guarantee: with n labelled calibration trajectories the deployed plug-in policy's regret decays as 1/sqrt(n). A controlled simulation study confirms each prediction of the theory, including the predicted 1/sqrt(n) rate. We additionally report a real-model validation on a Qwen2.5-Coder 1.5B->7B code cascade (MBPP, 257 tasks), confirming two of three pre-registered predictions: the escalation frontier dominates post-hoc routing at equal cost, and the cumulative competence belief's discrimination rises over generation.
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Artificial Intelligence
EMRB: A Multi-Level Benchmark for Evaluating LLM Reasoning over Raw Electromagnetic Signals
Mingxu Zhang, Ying Sun, Yuhan Li et al. (+4 more)
Large
language models (LLMs) are increasingly used as code
agents for scientific and engineering analysis, but their ability to analyze raw physical-layer measurements remains untested. We introduce EMRB (Electromagnetic
Reasoning Benchmark), which evaluates whether LLMs can analyze raw I/Q data by writing and running code. EMRB contains 200 problems across five difficulty levels and 27 question types, from signal detection to OFDM design, generated from 11 signal types with verified ground truth. Unlike
benchmarks built on preprocessed features or structured tables, EMRB provides only the raw capture; the quantities each question refers to must first be discovered through code. We evaluate 14 LLMs spanning proprietary, open-weight, and
reasoning-oriented families. Scores range from 24.1% to 78.9%, with the mean dropping from 84.9% on basic measurement to 21.2% on system design. We also propose ReconPilot, a structured method that separates signal reconnaissance, targeted analysis, and self-verification. Across three backbones, ReconPilot raises the overall score by 3.8 to 17.6 points and improves 13 of 15 backbone-level combinations tested. All data and code are publicly released in \href{https://github.com/mingxuZhang2/EMRB}{\textcolor{blue}{our GitHub repository}}.
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All authors Mingxu Zhang, Ying Sun, Yuhan Li, Yang Ji, Dazhong Shen, Ke Zhang, Shan Huang
Artificial Intelligence · Cross-listed from cs.CL
PARTAB: Partition-Aware Reasoning with Structured Evidence for Scalable Table Understanding
Md Mahadi Hasan Nahid and Davood Rafiei
Large
Language Models (LLMs) have shown strong capabilities in table
reasoning, but their effectiveness degrades as tables grow in size and complexity due to irrelevant context and difficulty localizing the evidence required for
reasoning. Existing approaches typically reason over either the full table or a single reduced view, which can still obscure important row-column relationships. We introducePARTAB (Partition-Aware
Reasoning overTables), a framework that constructs a structured evidence interface between the LLM and the table. PARTAB represents query-relevant evidence as semantically coherent, row-linked table regions and performs hierarchical selection over column groups and row-level partitions before composing the selected evidence for answer generation. We evaluate PARTAB on multiple table
reasoning benchmarks, covering question answering, fact verification, and numerical
reasoning. PARTAB consistently improves over full-table prompting and several recent table
reasoning methods, achieving strong performance on WikiTableQuestions and TabFact while remaining competitive on numerical
reasoning. Additional analyses show that semantic partitioning and targeted evidence selection improve evidence localization, substantially reduce the
reasoning context, and provide larger benefits on complex tables. These results demonstrate the value of structured, partition aware evidence construction for scalable table
reasoning.
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Artificial Intelligence · Cross-listed from cs.CL
When Less Is More: An Empirical Study of Minimal Responses in Counseling Dialogues and the Behavior of LLMs
Zhiyang Qi
In psychological counseling, effective support is not always delivered through long, information-rich responses. Minimal responses, such as backchannel cues and concise empathic statements, help convey attentive listening, express empathy, and encourage clients to continue expressing themselves. However, existing counseling dialogue systems and evaluation frameworks often favor explicit, content-rich replies, overlooking the interactional value of brief counselor utterances. This paper presents a systematic cross-lingual analysis of minimal responses across multiple counseling dialogue
datasets. We develop a two-stage filtering method based on utterance length and content, followed by contextual verification using a large
language model (LLM). Our analysis shows that minimal responses are common in human-collected
datasets but substantially underrepresented in LLM-generated ones. We further evaluate current LLMs in manually curated dialogue contexts where human counselors used minimal responses. The results show that strong commercial LLMs are capable of generating minimal responses when explicitly instructed, but still struggle to determine when such responses are appropriate. Counseling-specific models trained on synthetic data perform particularly poorly, tending instead to produce longer and more information-rich responses. Moreover, LLM-based response-quality evaluation may undervalue minimal responses, even when they are interactionally appropriate.
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Artificial Intelligence
AgentWorld: Personality-Aware Reliability Evaluation for Agentic Information Retrieval
Gunja Agarwal, Arup Kumar Das, Arun Menon et al. (+2 more)
Evaluation of agentic information
retrieval remains limited to scripted interactions with uniform users, missing both natural personality diversity and
adversarial brittleness. We present AgentWorld, a simulation framework combining (i)Big Five (OCEAN) personality-driven user populations with stateful tool-use environments; (ii)the pass$^k$ consistency metric with structured fault classification, partial-credit scoring, and dual-control handoff verification; (iii)score-thresholded training-data export in six fine-tuning formats; and (iv)an
adversarial Risk Analyser that snapshots required-intermediate-state spines, branches Monte-Carlo rollouts under four task-aware perturbation types, and quantifies risk via $\Delta P / \Delta T$ scoring, Dempster–Shafer evidence fusion, and Shapley attack-category attribution. Three experiments demonstrate the framework: a conversational analytics
agent across 10 OCEAN personas (240 evaluator judgments); a customer-support
agent across 5 tasks $\times$ 4 persona variants; and
adversarial stress-testing of 5 tasks revealing pre-existing trajectory brittleness ($V_{\min}=0.375$ without perturbation) and tool/infrastructure-layer attack dominance (Shapley: 46% system, 38% action). Personality variation surfaces failure modes uniform testing cannot expose—cross-domain leakage, contextual drift, a 0.27-point quality gap, and 50% vs. 100% pass-rate across personas on the same task—while the Risk Analyser quantifies trajectory-level brittleness that pass$^k$ alone cannot measure.
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All authors Gunja Agarwal, Arup Kumar Das, Arun Menon, Jitesh Chandra Mishra, Vignesh Divakaran
Artificial Intelligence · Cross-listed from cs.NE
ORBITALIF: An Efficient Spiking Federated Learning Framework for Onboard Cloud Removal
Bohan Zhang, Chenyu Xu, Yijie Mao et al. (+1 more)
Low-earth-orbit (LEO) satellites enable high-resolution, large-scale Earth observation for applications such as disaster monitoring and environmental surveillance. However, cloud coverage often obscures the Earth's surface, and conventional cloud-removal pipelines that download cloudy images to ground stations for processing suffer from limited contact windows, constrained satellite-to-ground bandwidth, and high latency. In this work, we propose a novel satellite
federated learning framework for cloud removal across LEO constellations, named orbital
attention leaky integrate-and-fire (OrbitALIF). OrbitALIF performs both onboard training and inference using a compact 2.30,M-parameter spiking neural network (SNN) backbone with an adaptive gated fusion module (AGFM) and a spectral-spatial hybrid
attention module (SHAM), combined with a decentralized
federated learning strategy that shares model weights via inter-satellite links. Our experiments show that OrbitALIF achieves competitive cloud removal quality while consuming only 0.287,mJ per inference on neuromorphic hardware, a 72.3 times (98.6%) energy reduction versus an equivalent artificial neural network (ANN).
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All authors Bohan Zhang, Chenyu Xu, Yijie Mao, Yuanming Shi
Artificial Intelligence
Compression Trinity: Exploring Sparsity, Quantization, and Low-Rank Approximations for LLM Compression
Mohammad Mozaffari
Prohibitive computational and environmental costs impede the scalable deployment of Large
Language Models (LLMs). Traditional compression techniques (sparsity, quantization, low-rank approximations) are typically applied in isolation, and each hits an accuracy-efficiency wall. This thesis proposes the "Compression Trinity," a unified framework that applies the three pillars jointly: sparsity to reduce computation, quantization to minimize
memory bandwidth, and low-rank approximations to recover accuracy. To accelerate pretraining, we apply the Trinity to the optimizer and model architecture. MKOR approximates
curvature via block-diagonal sparsity and low-rank inversion, maintaining numerical stability for quantized states; it reduces
curvature update complexity from $O(d^3)$ to $O(d^2)$ and accelerates convergence by up to 1.85x over KFAC. SLoPe accelerates training by up to 1.25x via a double-pruned backward pass for N:M sparsity, using low-rank "lazy" adapters in the final 1% of training to recover accuracy. For post-training compression, OPTIMA stabilizes static masks in a zero-training regime by formulating weight reconstruction as globally optimal column-wise quadratic programs, improving zero-shot accuracy by up to 3.97%. Given a fine-tuning budget, PATCH breaks the ceiling of static masks by learning a dynamic hybrid sparsity ratio between 0% and 50%, yielding up to 1.38x speedups. Finally, SLiM realizes the full Compression Trinity in one shot, using mathematically derived low-rank adapters to recover information lost to quantization and sparsity, improving accuracy by up to 5.66% over state-of-the-art methods and outperforming uncompressed dense models at equal parameter budgets by 0.6%. Together, these results show that jointly applying the Compression Trinity is essential for efficient, scalable, high-performance LLMs.
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Artificial Intelligence
Poisoning Agentic Alpha: Adversarial Vulnerabilities Across Roles and Architectures in Multi-Agent Trading Systems
CheolWon Na, Hao Ni, Lukasz Szpruch et al. (+7 more)
LLM-based multi-
agent trading systems, in which specialized
agents collaborate through structured communication to produce trading decisions, are moving rapidly from research prototypes to live deployments that control real assets. The same inter-
agent communication that makes them effective also exposes them: a corrupted signal can propagate to the final decision and translate into realized financial loss. Unlike prior attacks that presume privileged access to system internals, we restrict the adversary to what is practically reachable—the source data and
prompts agents consume—yielding a low-barrier, and thus democratized threat model instantiated as role-specific adversaries. We present the first systematic empirical study in the financial domain to characterize how an
adversarial signal enters a multi-
agent trading system and how far it survives toward the decision. Along the role axis, we decompose a widely-used trading pipeline into four functional roles—Analyst, Researcher, Trader, and Risk Manager—and pair each with an attack matched to its interface. Along the structural axis, we evaluate four communication
topologies under data- and
agent-level attacks, using the
Adversarial Signal Preservation Score (APS) as a post-hoc lens on why some designs are more robust than others. We conduct experiments across five assets, two backbones, and two target directions. A central finding is that no architecture is inherently robust. These findings provide insights for the future design of safer and more robust agentic trading systems.
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All authors CheolWon Na, Hao Ni, Lukasz Szpruch, Zhangyang Wang, Dhagash Mehta, Saurabh Nagrecha, Alejandro Lopez-Lira, Chanyeol Choi, Yongjae Lee, Jee-Hyong Lee
Artificial Intelligence · Cross-listed from cs.LG
Mechanistic Circuit Identification for Controllable Data Generation
Nakyung Lee, Sangwoo Hong and Jungwoo Lee
While recent advances in data synthesis aim to curate high-quality
datasets, most generation pipelines still rely on heuristic
prompt-based control. This black-box paradigm provides limited insight into how individual samples interact with a model's underlying learning dynamics. To bridge this gap, we propose a
circuit-grounded framework that connects training-dynamics-based data valuation with mechanistic interpretability (MI). Specifically, we conceptualize data quality along three complementary utility axes, learnability, challenge, and
alignment. First, we uncover specialized model-internal
circuits that causally govern these utility signals. Then, moving beyond heuristic prompting toward mechanistic control, we leverage these
circuits as controllable interfaces, actively steering generation to produce utility-targeted data. Building on this capability, we introduce SAMS (Stage-Aware Mechanistic Scheduling), which schedules
circuit-steered data according to the model's evolving optimization needs. Experiments on multiple-choice QA tasks demonstrate that our approach yields precisely controlled data with greater diversity than
prompt-based baselines, consistently improving downstream performance and calibration. Ultimately, this work establishes a principled white-box paradigm for interpretable data generation, pioneering the use of MI not just as an analytical tool, but as a practical, controllable interface.
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Artificial Intelligence · Cross-listed from cs.CV
VisCache: Visual KV Cache Pruning for Efficient Vision Large Language Model Inference
Lyuke Wang, Zhuo Li and Guangxu Zhu
While
Vision Large
Language Models (VLLMs) have achieved remarkable success in
multimodal reasoning, their long-context inference remains prohibitively expensive due to the massive computation and
memory overhead of visual Key-Value (KV) caches. Existing KV compression methods often apply uniform
pruning across visual
tokens and layers, leading to substantial information loss and degraded performance.To address this challenge, we propose VisCache, a plug-and-play framework for coarse-to-fine Visual KV Cache
pruning without training, which consists of two synergistic stages. First, a lightweight VLM filters temporal redundancy by selectively forwarding semantically informative keyframes. Second, we introduce {PruneKV}, a
surgical KV compression
algorithm tailored to the
attention dynamics of VLLMs. Unlike rigid
pruning strategies, PruneKV adopts a
parabolic layer-wise budget allocation together with an asymmetric update mechanism that selectively prunes keys while fusing values, thereby preserving critical contextual information. Extensive experiments demonstrate that VisCache substantially improves inference efficiency, achieving up to {2.35$\times$ speedup} and significant
memory reduction while maintaining competitive performance with only {19–28%} KV cache retention. VisCache consistently outperforms existing baselines, establishing a new Pareto frontier between efficiency and performance for long-context VLLM inference. Code is available at https://github.com/Wlklk/VisCache
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Artificial Intelligence · Cross-listed from cs.SD
Don't Just Listen, Try Planning: Graph-based Retrieval-Generation Agent for Long-form Audio Meeting Understanding
Quanwei Tang, Dong Zhang, Shoushan Li et al. (+1 more)
While long-form audio meeting understanding (LAMU) is garnering growing
attention, task-specific question answering (QA)
datasets remain scarce. Existing
speech QA paradigms and state-of-the-art
Speech LLMs suffer from acoustic information loss and poor long-term context
memory. To address these issues, we construct the LongAudioQA
dataset and propose the GRGA model, which models heterogeneous audio features into a multi-dimensional graph and leverages
agent planning for
retrieval and answer generation.
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All authors Quanwei Tang, Dong Zhang, Shoushan Li, Guodong Zhou
Artificial Intelligence
Algorithmic Impact Reveals the Hidden Social Choice Structure of Alignment
Zachary Wojtowicz, Michelle Si, Finale Doshi-Velez et al. (+1 more)
When an AI
algorithm makes decisions that affect more than one person, aligning it becomes a problem of social choice: how should people's divergent preferences about system behavior be reconciled and aggregated into a single coherent model? The standard approach to aligning frontier AI models$\unicode{x2013}$reinforcement learning from human feedback$\unicode{x2013}$largely sidesteps this question and has poor social choice guarantees. However, it remains unclear what alternative should replace it. We show that, by focusing directly on an
algorithm's welfare consequences, the
alignment problem can be reformulated as linear optimization over a convex impact space, which makes it amenable to the standard toolkit of welfare economics and mechanism design. This reformulation clarifies how
alignment protocols translate into welfare consequences and, conversely, how a social planner's desired constraints on welfare consequences can be translated back into
alignment protocols. We apply this transformation to show that voting-by-issues and random-dictatorship mechanisms are strategyproof and unanimous. Demonstrating the reverse direction, we also apply the impact representation to derive a family of
alignment protocols that maximize utilitarian social welfare subject to various social desiderata, such as bounds on individual or group harm. We illustrate the welfare implications of these
alignment protocols empirically using real human preferences over kidney allocation, charitable food distribution, LLM responses, and trolley problems.
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All authors Zachary Wojtowicz, Michelle Si, Finale Doshi-Velez, Ariel Procaccia
Artificial Intelligence · Cross-listed from cs.RO
Hierarchical Skill Retrieval for Data-Efficient Adaptation of Vision-Language-Action Models
Haoran Hao, Shahram Najam Syed, Jeff Schneider et al. (+1 more)
While
Vision-
Language-Action (VLA) models pretrained on large-scale
robot datasets provide a strong foundation for
robot manipulation, their performance can degrade when adapted to new tasks with limited task-specific demonstrations.
Retrieval offers a practical way to reuse existing demonstrations for data-efficient adaptation, but existing methods often rely on visual similarity, state-action representations, or task-level
language matching. These approaches may overlook the hierarchical structure of long-horizon manipulation tasks, where complete task matches are rare but reusable skills are often abundant. To address this challenge, we propose Hierarchical Skill
Retrieval (HSR), a
retrieval framework for data-efficient VLA adaptation. Specifically, HSR first decomposes a target task into candidate skill sequences. It evaluates each plan based on both semantic plausibility and skill reliability estimated from the prior
dataset. The selected decomposition is then used for hybrid
retrieval. This combines subtask-level
language retrieval with behavior-feature reranking to identify demonstrations that are both semantically relevant and compatible with the target task. Finally, we adapt the policy through a two-stage pretraining and finetuning pipeline, which separates general skill acquisition from task-specific adaptation. Experiments on the LIBERO
benchmark and several real-world
robot manipulation tasks show that HSR improves the average success rate by 10.3% and 21.3% over the strongest baseline, respectively. These results demonstrate the effectiveness of structured skill-level
retrieval for data-efficient VLA adaptation.
Videos and code are available at https://hoar012.github.io/HSR-Project.
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All authors Haoran Hao, Shahram Najam Syed, Jeff Schneider, Jeffrey Ichnowski
Artificial Intelligence
Relative Time Intervals Representation for Word-level Timestamping with Masked Training
Quanwei Tang, Zhiyu Tang, Xu Li et al. (+3 more)
Although
Speech Large
Language Models (SpeechLLMs) excel at
speech understanding and generation, their capacity for fine-grained, temporally aligned outputs remains underexplored. Our work addresses this gap by enabling SpeechLLMs to jointly model
speech content and temporal structure, effectively transforming them from “content understanding machines" into “temporal-aware content understanding machines". Specifically, we replace traditional absolute timestamps with relative timestamps, achieving a more compact vocabulary and stronger generalization capabilities. To efficiently infuse timestamp prediction ability into pre-trained large
language models, we introduce a hybrid fine-tuning strategy: full-parameter fine-tuning of the timestamp-augmented
embedding layer and
language model head, combined with LoRA fine-tuning of the decoder layers. Moreover, we design a masked timestamp training objective, preventing the model from over-relying on ground-truth timestamps, and thereby enhancing robustness against noisy real-world annotations. Extensive experiments demonstrate that our approach achieves significant improvements in timestamp prediction accuracy while maintaining strong
speech transcription performance.
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All authors Quanwei Tang, Zhiyu Tang, Xu Li, Dong Zhang, Shoushan, Guodong Zhou
Artificial Intelligence · Cross-listed from cs.RO
Design-to-Plan: A Large Language Model-Based Multi-Agent Framework for Manufacturing Process Planning from 3D CAD Models and 2D Engineering Drawings
Muhammad Tayyab Khan, Lequn Chen, Wenhe Feng et al. (+1 more)
Manufacturing process planning transforms heterogeneous design information into coherent manufacturing decisions. However, existing approaches focus on isolated subtasks, such as feature recognition, drawing interpretation, or tool selection, and struggle to support the full
reasoning chain from design artifacts to process plans. This is critical when planning must interpret 3D CAD models, 2D engineering drawings, materials, and domain-specific rules. To address this gap, this paper presents Design-to-Plan, a large
language model (LLM)-based multi-
agent framework for end-to-end manufacturing process planning. An orchestrator coordinates specialized
agents for 3D feature recognition, 2D drawing analysis, 2D-3D context fusion, knowledge
retrieval, process sequencing, tool selection, and report generation. Rather than using LLMs as standalone text generators, the framework deploys them as
reasoning agents that interact with deterministic modules and knowledge sources to produce consistent and traceable decisions. In this hybrid design, deterministic modules and specialized
agents extract structured information from CAD and drawing inputs, while LLM
agents perform context-aware
reasoning, retrieve manufacturing rules, resolve conflicts, and generate planning outputs. The framework is evaluated using 300
benchmark cases across three downstream ReAct-enabled
agents, plus separate evaluations of CAD feature recognition, drawing analysis, and 2D-3D context fusion. The parallel architecture achieves 100% success across downstream
agents, Tool F1 scores of 95.9%-97.6%, 90% source detection accuracy in conflict analysis, and a 60%-68% reduction in
token usage for key planning tasks. Results show that structured LLM-based multi-
agent coordination can bridge design representations and manufacturing knowledge, enabling scalable, efficient, and traceable design-to-plan automation.
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All authors Muhammad Tayyab Khan, Lequn Chen, Wenhe Feng, Seung Ki Moon
Artificial Intelligence · Cross-listed from cs.LG
ChorusTIC: Training-Free Multivariate Time Series Classification via Chorus In-Context Learning
Juntao Fang, Shifeng Xie, Ruichu Cai et al. (+6 more)
Time series classification underpins applications in healthcare, sensing, and industrial monitoring. Although time series foundation models support
forecasting and transferable representation learning, classification still typically requires fitting a task-specific classifier on each target
dataset, while individual channels of multivariate inputs are often encoded independently. We introduce ChorusTIC, a classification-native foundation model for in-context classification across heterogeneous channel configurations without target-task parameter updates. ChorusTIC combines episode-consistent Random Subchannel Slot Concatenation with a shared dual-axis encoder to model temporal and cross-channel interactions and map variable channel configurations into a fixed-width representation independent of the original channel count. It then calibrates feature axes using context-derived distributions and predicts query labels through leakage-protected in-context learning. We pretrain ChorusTIC solely on synthetic labeled episodes comprising context and query sets that share a task background, with classes distinguished by sparse temporal or cross-channel rules. Evaluations on the complete UEA-30 and UCR-128 archives show strong full-context and low-label performance without target-specific classifier fitting.
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All authors Juntao Fang, Shifeng Xie, Ruichu Cai, Shengji Zheng, Zijian Li, Keli Zhang, Lujia Pan, Themis Palpanas, Zhifeng Hao
Artificial Intelligence
Beyond Confidence: Test-Time Scaling for Multi-Turn Search Agents via Retrieval Grounding
Hyunho Kook, Junhyuk So, Tianyu Fu et al. (+2 more)
Confidence-based voting aggregates parallel LLM rollouts by weighting each with internal signals such as
token log probabilities, and has been actively studied for single-turn
reasoning. However, modern LLMs increasingly act as multi-turn search
agents that retrieve and condition on external documents. In this paper, we show that confidence-based voting transfers poorly to this multi-turn setting, and identify the underlying failure reason as copy inflation: when retrieved documents are appended to an
agent's context,
tokens copied from those documents receive systematically inflated log probabilities. This flattens confidence scores within each question and weakens the resulting weighted vote. To address this issue, we propose
Retrieval-Grounded Voting (RGV), which scores each rollout by the lexical overlap between its final answer and the documents it retrieved. By computing the signal outside the contaminated context, RGV sidesteps both
token log probabilities and additional LLM calls. Across four search-
agent benchmarks and five LLMs, RGV consistently outperforms confidence-based voting, with gains of up to +5.4% accuracy and +35% on minority-correct questions, where the correct answer appears in only 1-2 of 8 rollouts.
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All authors Hyunho Kook, Junhyuk So, Tianyu Fu, Haizhong Zheng, Beidi Chen
Artificial Intelligence · Cross-listed from cs.CR
What Guides the Agent? Adjudicating Unauthorized Behavior via Localizing Behavior-Guiding Instructions
Yichao Gao, Yumo Zhang, Yunhao Yao et al. (+4 more)
LLM
agents integrated with external resources gain complex task capabilities, yet the unified natural-
language context channel makes them vulnerable to injection attacks: untrusted external data may be dynamically parsed as behavior-guiding instructions during LLM inference, thereby subverting the
agent's decision. Existing defenses focus on static detection or isolation of malicious content at the input/output level, remains insufficient for detecting such dynamic inducements that arise during model
reasoning. We propose Attnlocate, a runtime framework for fine-grained localization of context spans that genuinely influence tool-calling decisions, i.e., behavior-guiding instructions. Attnlocate casts this localization problem as an object detection task, aiming to detect the distinctive activation traces induced by behavior-guiding instructions within the
attention matrix. Specifically, we design a multi-head, multi-layer
attention aggregation scheme to construct a
token-level feature space tailored for object detection. Then, a 1-D U-Net equipped with an anchor-free detection head is deployed to detect these spans. Finally, based on the authority of the provider from which the detected behavior-guiding spans originate, Attnlocate dynamically adjudicates malicious invocation attempts. We evaluate Attnlocate across ten
agent configurations from five LLM families, covering scenarios involving indirect
prompt injection and tool poisoning. Attnlocate achieves a mean IoU of 0.743, an average AUROC of 0.956, and a 0.934 true-positive rate at 0.067 false-positive rate. It also transfers effectively across unseen models and supports authority policy adaptation without retraining.
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All authors Yichao Gao, Yumo Zhang, Yunhao Yao, Haohua Du, Puhan Luo, Ruiqi Li, Zhiqiang Wang
Artificial Intelligence · Cross-listed from cs.CV
IterCAD: Iterative Program Repair for CAD Code Generation from Orthographic Views
Yuchuan Wu, Ke Niu, Haiyang Yu et al. (+3 more)
Generating executable parametric CAD code from dimension-annotated orthographic drawings is a challenging task requiring
geometric understanding, procedural
reasoning, and precise numerical prediction. Existing
vision-
language approaches typically formulate this problem as one-shot generation, preventing the model from inspecting intermediate CAD results and correcting early mistakes, often leading to non-executable code or geometrically inconsistent outputs. In this paper, we propose IterCAD, an iterative framework that reformulates orthographic-view-to-CAD generation as a progressive program repair process. Instead of predicting the final CAD code in a single pass, IterCAD repeatedly analyzes the current CAD result, reasons about its discrepancy with the target views, and explicitly decides whether to REVISE the code or STOP the refinement process. To make iterative repair learnable, we further construct IterCAD-RS, a structured revise-or-stop supervision set containing both repairable intermediate CAD states and already-correct states, and develop a three-stage training strategy for initial generation, revision learning, and multi-turn RL optimization. By closing the loop between visual understanding,
geometric verification, and code refinement, IterCAD progressively corrects structural and parametric errors. Experiments on CADExpert show that IterCAD consistently improves code executability and
geometric fidelity over strong one-shot baselines.
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All authors Yuchuan Wu, Ke Niu, Haiyang Yu, Zhuofan Chen, Xiangyang Xue, Bin Li
Artificial Intelligence · Cross-listed from cs.CR
WebMCP-Phalanx: Enforcing and Characterizing Trust Boundaries for Browser-Integrated LLM Agents
Lin-Fa Lee, YI-YU Chang and Kuo-Hui Yeh
The emerging W3C WebMCP proposal enables LLM
agents to invoke tools exposed by web pages. In multi-party web environments, however, integrating
agent execution into a browser security model centered on the Same-Origin Policy (SOP) leaves insufficient provenance and lifecycle guarantees for
agent-accessible tools, creating three risks: subject-attribution spoofing, uncontrolled tool lifecycles, and semantic
prompt injection. We propose WebMCP-Phalanx, a dual-layer
agent runtime architecture. Its first layer provides a browser-native trust anchor that binds each tool to its registering principal through cryptographically protected capability credentials and propagates provenance labels throughout the tool lifecycle. Its second layer separates semantic inspection from privileged tool use. A Quarantine
Agent (Q-LLM), without tool invocation authority, inspects tool metadata, outputs, and page-supplied content for
prompt injection. Validated content is then forwarded to a Privileged
Agent (P-LLM) for execution, while the Q-LLM's internal state remains hidden from page scripts. Empirical evaluation shows that the browser-native ownership mechanism reduces revocation and overwrite attack success from 100% to 0%. The dual-
agent runtime blocks all 80
prompt-injection attempts embedded in tool descriptions and limits tool-return attacks to 2 successful cases out of 80. Across experiments, task utility remains statistically indistinguishable from the no-attack baseline. Under a white-box adaptive attacker, however, description-based filtering can be bypassed through malicious tool names invoked before inspection. This finding motivates a call-timing gate that delays tool invocation until all
agent-visible tool metadata has been validated.
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Artificial Intelligence
Reflection with Action-Induced Visual Differences for Desktop GUI Agents
Yijie Ma, Chaoyue Niu, Fan Wu et al. (+1 more)
The Planner-Operator-Reflector (POR) framework is widely used in GUI
agents to maintain objective
alignment in complex tasks through modular collaboration. However, desktop GUIs introduce a key challenge: large, dense interfaces often exhibit subtle or scattered state changes, placing most of the burden on the reflector, which must compare pre- and post-action screens, while the planner and operator reason over a single state. Existing reflectors collapse change detection and outcome verification into one step, leaving evidence implicit and yielding weakly grounded decisions. To address this limitation, we propose Evidence-First Reflection (EFR), a two-stage reflector that explicitly decouples action-induced visual differences extraction from outcome verification. EFR identifies the action location and candidate changed regions with Set-of-Marks annotations, describes and filters action-relevant changes, and makes the final judgment from the cleaned evidence. This evidence-
reasoning decoupled design makes reflection better grounded in screen transitions, while reducing both visual search complexity and
reasoning burden. Experiments on OSWorld-Verified and WindowsAgentArena demonstrate that EFR improves reflector accuracy by 7.11%, yielding average end-to-end task success gains of 5.94% and 4.95% on the two
benchmarks, respectively.
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All authors Yijie Ma, Chaoyue Niu, Fan Wu, Guihai Chen
Artificial Intelligence · Cross-listed from cs.CL
SAGE: From Direct Answering to Evidence-Grounded Inference for Chinese Ancient Document Understanding
Yuchuan Wu, Xuan Luo, Yinglian Zhu et al. (+3 more)
Chinese ancient document understanding demands complex visual, linguistic, and historical
reasoning. Current Large
Vision-
Language Models (LVLMs) typically rely on an opaque, single-pass generation paradigm, often producing overconfident and weakly grounded responses. To address this, we propose SAGE, an evidence-grounded multi-
agent framework that reformulates Chinese ancient document understanding as evidence-grounded inference rather than direct answer generation. SAGE coordinates specialized
agents for task-aware planning, tool-mediated evidence acquisition, claim-level verification, and bounded replanning under a constrained shared-state runtime. This design supports bounded evidence seeking, answer revision, and abstention when grounding is insufficient. Experiments on the AncientDoc
benchmark show that SAGE consistently outperforms matched direct-answering baselines across three LVLM backbones. Remarkably, SAGE with Qwen3.5-9B surpasses much larger monolithic LVLMs on most evaluated metrics, highlighting the importance of structured, evidence-grounded inference beyond model scaling.
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All authors Yuchuan Wu, Xuan Luo, Yinglian Zhu, Meng Fang, Xiangyang Xue, Bin Li
Artificial Intelligence
Incorporating Cognitive Load and Knowledge Transfer for Multi-Domain Knowledge Tracing
Haotian Zhang, Shucun Wang, Jinze Wu et al. (+6 more)
Knowledge Tracing (KT) aims to assess students' dynamic knowledge states from their learning histories. While most existing KT methods focus on single-domain learning with notable success, real-world learning scenarios often involve multiple domains simultaneously, introducing two critical factors: 1) Cognitive load, arising from managing learning across domains in both temporal and knowledge dimensions. 2) Knowledge transfer, where knowledge states in one domain influence related states both within and across domains. In this paper, we focus on exploring these factors to improve students' knowledge state assessment in multi-domain learning scenarios and propose a novel method incorporating cognitive Load and knowledge Transfer for Multi-domain Knowledge Tracing (LT-MKT). Specifically, to bridge isolated domains, LT-MKT first integrates textual information from questions and their associated concepts to construct a Multi-domain Hierarchical Graph, leveraging the advanced representational capabilities of large
language models (LLMs). Then, cross-domain features in both the temporal and knowledge dimensions are explicitly modeled to capture the effects of cognitive load. Additionally, a knowledge transfer module is designed to model the propagation of knowledge states within and across domains. By jointly modeling these factors, LT-MKT enables more accurate prediction of students' future performance. Finally, extensive experiments on real-world
datasets demonstrate that our method achieves state-of-the-art performance.
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All authors Haotian Zhang, Shucun Wang, Jinze Wu, Liang Ding, Shuochen Liu, Zhenya Huang, Jing Sha, Shijin Wang, Qi Liu
Artificial Intelligence
Diverse by Reasoning: Harnessing the Wisdom of LLM Crowds for Future Prediction
Nirupam Chetlapalli, Yiming Liao, Min-Chun Chen et al. (+1 more)
Large
language models (LLMs) are increasingly used for future prediction, motivating the use of multiple models as a wisdom-of-the-crowd mechanism. However, simply increasing crowd size does not guarantee effective diversity, as different LLMs may exhibit redundant behaviors. We propose a behavior-aware framework for constructing diverse LLM crowds. The framework characterizes models using their
reasoning traces on independent development tasks, clusters models by behavioral similarity, and selects representatives for collective prediction. We evaluate 25 LLMs using seven development
benchmarks for behavioral diversity modeling and two future-prediction
benchmarks for evaluating diverse crowds' performance. Our results show that crowd composition can matter more than crowd size: a three-model medoid crowd based on K-means++ behavioral clustering outperforms conventional voting over all 25 models on both prediction
benchmarks, while reducing model calls by 88% and inference cost by approximately 80%. The results further suggest that representative behavioral diversity, rather than simply maximizing diversity, is important for constructing effective LLM crowds
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All authors Nirupam Chetlapalli, Yiming Liao, Min-Chun Chen, Keke Chen
Artificial Intelligence · Cross-listed from cs.IR
Hybrid Semantic Tool Discovery for Enterprise MCP Gateway: Architecture and Implementation
Olympia Saha, Amy Wang and Srinivasan Manoharan
Large
language model (LLM)
agents invoke external tools to retrieve and reason over information beyond pretrained knowledge. The Model Context Protocol (MCP) standardizes how such tools are surfaced, and a proxy MCP server aggregates many backend servers behind a single endpoint providing a secure, governable chokepoint for authentication, policy enforcement, and observability. This architecture creates two compounding challenges: a context-engineering bottleneck where full tool schemas saturate the model context window before any user query, and a tool discoverability barrier where users and
agents cannot identify the best tool among 2,000+ indexed tools across 200+ MCP servers.
Prompt caching reduces reprocessing cost but neither frees context capacity nor improves accuracy. We present SCOUT (Selective Context Optimization for Universal Tooling), which reframes tool exposure as a context-selection problem, injecting only tools relevant to the current step. SCOUT surfaces two MCP meta-tools – tool_search and execute_tool – where tool_search performs hybrid
retrieval, fusing BM25 sparse matching with dense vector search via Reciprocal Rank Fusion to return the top-k relevant tools. Backed by zero-downtime catalog update pipelines, SCOUT resolves both context saturation and tool discovery challenges. In production at PayPal, SCOUT reduces MCP tool-
token consumption from 140.2k
tokens (70.1% of context) to 1.3k
tokens (0.8%), a 99% reduction, cutting per-query inference cost at enterprise scale. Because SCOUT is surfaced as standard MCP tools, it is model-agnostic and requires no client-side modifications.
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Artificial Intelligence · Cross-listed from math.OC
The Shadow Price of Intelligence: Quality Degradation in LLM Inference as a Supply Chain Problem
Elioth Sanabria
Large
language model providers are compute constrained, and a common response to congestion is to degrade service. A degraded answer fails with some probability, and a failed answer either returns as a retry or departs as churn, destroying LTV on an unaccounted ledger. We model inference allocation as a newsvendor whose stockout cost is churned lifetime value, a
geometric retry multiplier in which the recycled product is dissatisfaction, and a two-regime transient fluid queue whose arrival rate is made endogenous by retries. Statically, there is a regime in which a cheaper model saves energy per initiated task while consuming strictly more capacity per initiated task, so the discount inverts when capacity binds. Dynamically, a reactive throttle fired during a surge can cross an ignition threshold beyond which it manufactures more traffic than it sheds, and a release rule set below the degraded
equilibrium converts a transient surge into a permanent degraded regime. With heterogeneous customers, throttling is a transportation problem in retry-inflated load whose optimal policy rations intelligence by critical ratio, and whose dual, the shadow price of intelligence, prices a marginal query by class and by hour. Under congestion, throttling is not a cost lever but a demand lever.
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Artificial Intelligence
Memory Is Not Always Needed: Characterizing Conditional Memory in Scientific Reasoning
Zhen Bi, Xueshu Chen, Yan Wang et al. (+6 more)
Scientific
reasoning requires
language models to retrieve specialized knowledge and incorporate it reliably into multi-step computation. Conditional
memory provides an explicit lookup pathway that complements dense neural representations, but its usefulness is inherently input- and computation-dependent: retrieved information may repair missing scientific associations, yet it may also introduce distracting shortcuts or interfere with
reasoning that the base model can already perform correctly. In this work, we systematically investigate when, where, and to what extent conditional
memory should participate in scientific
reasoning. We characterize the scientific knowledge boundary and controlled interventions on
memory-enabled knowledge-
circuit nodes. Based on these analyses, we propose a Knowledge Boundary-Aware Router that uses task-specific input proxies available before generation to determine whether
memory is activated, which layer-stage nodes receive
memory signals, and how strongly these signals contribute. Experiments on biological and chemical
reasoning benchmarks, covering two backbone families and six task types, show that
memory effects vary substantially across inputs, tasks, and injection locations. Compared with static and activation-rate-matched random routing, our approach more consistently preserves beneficial
memory contributions while suppressing
memory-induced regressions, establishing selective
memory allocation as an important principle for reliable scientific
reasoning.
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All authors Zhen Bi, Xueshu Chen, Yan Wang, Zhizhi Peng, Haosen Hong, Zhen Wang, Zhixuan Chu, Bingyu Zhu, Jungang Lou